Pattern recognition is to classify images taken by cameras or image scanners into a computer. A voice of an object can be picked up through a microphone and taken into a computer. When the object is a human being, his or her facial image or speech voice is to correspond to the object and then classified. Therefore, the pattern recognition technique is summarized to handle the following two variable factors:                (1) within-class scatter, i.e., showing different scatters in appearance depending on conditions at recording the object although images or voices are derived from the same object; and        (2) between-class scatter, i.e., showing different patterns due to different objects.        
In conventional art of the pattern recognition, the following method has been highly assessed: First, assume a sample space corresponding to a set of entire patterns, then apply consistent functions to individual input data so that the within-class scatter can be minimized and the between-class scatter can be maximized, thereby extracting features. The method bases on such a fundamental model. For instance, Fisher's discriminant analysis (Fukunaga: Introduction to Statistical Pattern Recognition, Academic Press, 1972) has been well known as a typical example, and it has been often used in the fields of character recognition, voice recognition and facial image recognition.
However, an assumption of Fisher's discriminant analysis method, i.e., a model of which entire patterns are derived from one distribution, seems sometimes unreasonable viewed from actual problems. For instance, when a system, which checks a face photo on an identification with a facial image taken by a video camera, is considered, this one is made by shooting directly an object and that one is taken indirectly from a printed material. These two materials are compared with each other for determining the identity of the two. However, an assumption that the sets of all the images formed through different processes are derived from one distribution is unreasonable because the images on these two materials differ too much. Actually, it is sometimes difficult for an operator to check a man himself against the photo on his identification.
It is thus concluded that Fisher's discriminant analysis method, i.e., entire patterns are described with one distribution and common feature-extraction-functions are consistently applied to input data, which is to be classified, for recognizing patterns, has a limit of accuracy.
The inventor of the present invention filed an application (Publication No. EP 0 944 018) with EPO in order to solve this problem. In this patent application, pattern sets A, B are prepared, where pattern set A is an image pattern directly taken a person's face by a video camera while pattern set B is read a photograph of the same person's face by an image scanner. Thus the two sets are formed of the same object but through different processes. From these two pattern sets, (1) distributions of the patterns are found, and (2) perturbation distribution of individual pattern corresponding between pattern sets A and B, is found, or perturbation distribution of sets of patterns in pattern set A corresponding to each element BI of pattern set B. Then a feature extraction matrix, which minimizes an overlapping cubic volume between the pattern distribution found in (1) and the perturbation distribution found in (2), is found. This feature-extraction-matrix is applied to pattern sets A and B, and each feature amount is calculated. Among the feature amounts, the elements mostly similar to each other are used for determining the identity.